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Machine learning-based chemical binding similarity using evolutionary relationships of target genes
Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To o...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6846180/ https://www.ncbi.nlm.nih.gov/pubmed/31504818 http://dx.doi.org/10.1093/nar/gkz743 |
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author | Park, Keunwan Ko, Young-Joon Durai, Prasannavenkatesh Pan, Cheol-Ho |
author_facet | Park, Keunwan Ko, Young-Joon Durai, Prasannavenkatesh Pan, Cheol-Ho |
author_sort | Park, Keunwan |
collection | PubMed |
description | Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To overcome this limitation, we developed a novel machine learning-based chemical binding similarity score by using various evolutionary relationships of binding targets. The chemical similarity was defined by the probability of chemical compounds binding to identical targets. Comprehensive and heterogeneous multiple target-binding chemical data were integrated into a paired data format and processed using multiple classification similarity-learning models with various levels of target evolutionary information. Encoding evolutionary information to chemical compounds through their binding targets substantially expanded available chemical-target interaction data and significantly improved model performance. The output probability of our integrated model, referred to as ensemble evolutionary chemical binding similarity (ensECBS), was effective for finding hidden chemical relationships. The developed method can serve as a novel chemical similarity tool that uses evolutionarily conserved target binding information. |
format | Online Article Text |
id | pubmed-6846180 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-68461802019-11-18 Machine learning-based chemical binding similarity using evolutionary relationships of target genes Park, Keunwan Ko, Young-Joon Durai, Prasannavenkatesh Pan, Cheol-Ho Nucleic Acids Res Methods Online Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To overcome this limitation, we developed a novel machine learning-based chemical binding similarity score by using various evolutionary relationships of binding targets. The chemical similarity was defined by the probability of chemical compounds binding to identical targets. Comprehensive and heterogeneous multiple target-binding chemical data were integrated into a paired data format and processed using multiple classification similarity-learning models with various levels of target evolutionary information. Encoding evolutionary information to chemical compounds through their binding targets substantially expanded available chemical-target interaction data and significantly improved model performance. The output probability of our integrated model, referred to as ensemble evolutionary chemical binding similarity (ensECBS), was effective for finding hidden chemical relationships. The developed method can serve as a novel chemical similarity tool that uses evolutionarily conserved target binding information. Oxford University Press 2019-11-18 2019-08-31 /pmc/articles/PMC6846180/ /pubmed/31504818 http://dx.doi.org/10.1093/nar/gkz743 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Methods Online Park, Keunwan Ko, Young-Joon Durai, Prasannavenkatesh Pan, Cheol-Ho Machine learning-based chemical binding similarity using evolutionary relationships of target genes |
title | Machine learning-based chemical binding similarity using evolutionary relationships of target genes |
title_full | Machine learning-based chemical binding similarity using evolutionary relationships of target genes |
title_fullStr | Machine learning-based chemical binding similarity using evolutionary relationships of target genes |
title_full_unstemmed | Machine learning-based chemical binding similarity using evolutionary relationships of target genes |
title_short | Machine learning-based chemical binding similarity using evolutionary relationships of target genes |
title_sort | machine learning-based chemical binding similarity using evolutionary relationships of target genes |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6846180/ https://www.ncbi.nlm.nih.gov/pubmed/31504818 http://dx.doi.org/10.1093/nar/gkz743 |
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